Optimizing execution of database queries containing user-defined functions

ABSTRACT

A query engine (or optimizer) which supports database queries having user-defined functions maintains historical execution data with respect to each of multiple user-defined functions. The historical execution data is dynamically updated based on query execution performance. When executing a query having user-defined functions, the query engine uses the historical execution data to predict an optimal evaluation ordering for the query conditions and, preferably, to dynamically adjust the evaluation order when appropriate. Preferably, the historical execution data includes historical execution time of the user-defined function and proportion of evaluated records which satisfied the query parameters.

CROSS REFERENCE TO RELATED APPLICATION

This is a continuation of pending U.S. patent application Ser. No.10/901,610, filed Jul. 29, 2004, entitled “Method and Apparatus forOptimizing Execution of Database Queries Containing User-DefinedFunctions”, which is herein incorporated by reference. This applicationclaims priority under 35 U.S.C. §120 of U.S. patent application Ser. No.10/901,610, filed Jul. 29, 2004.

This application is also related to U.S. patent application Ser. No.12/167,353, filed Jul. 3, 2008.

FIELD OF THE INVENTION

The present invention relates generally to digital data processing, andmore particularly to the generation and execution of database queries ina digital computer system.

BACKGROUND OF THE INVENTION

In the latter half of the twentieth century, there began a phenomenonknown as the information revolution. While the information revolution isa historical development broader in scope than any one event or machine,no single device has come to represent the information revolution morethan the digital electronic computer. The development of computersystems has surely been a revolution. Each year, computer systems growfaster, store more data, and provide more applications to their users.

A modern computer system typically comprises hardware in the form of oneor more central processing units (CPU) for processing instructions,memory for storing instructions and other data, and other supportinghardware necessary to transfer information, communicate with theexternal world, and so forth. From the standpoint of the computer'shardware, most systems operate in fundamentally the same manner.Processors are capable of performing a limited set of very simpleoperations, such as arithmetic, logical comparisons, and movement ofdata from one location to another. But each operation is performed veryquickly. Programs which direct a computer to perform massive numbers ofthese simple operations give the illusion that the computer is doingsomething sophisticated. What is perceived by the user as a new orimproved capability of a computer system is made possible by performingessentially the same set of very simple operations, but doing it muchfaster. Therefore continuing improvements to computer systems requirethat these systems be made ever faster.

The overall speed at which a computer system performs day-to-day tasks(also called “throughput”) can be increased by making variousimprovements to the computer's hardware design, which in one way oranother increase the average number of simple operations performed perunit of time. The overall speed of the system can also be increased bymaking algorithmic improvements to the system design, and particularly,to the design of software executing on the system. Unlike most hardwareimprovements, many algorithmic improvements to software increase thethroughput not by increasing the average number of operations executedper unit time, but by reducing the total number of operations which mustbe executed to perform a given task.

Complex systems may be used to support a variety of applications, butone common use is the maintenance of large databases, from whichinformation may be obtained. Large databases usually support some formof database query for obtaining information which is extracted fromselected database fields and records. Such queries can consumesignificant system resources, particularly processor resources, and thespeed at which queries are performed can have a substantial influence onthe overall system throughput.

Conceptually, a database may be viewed as one or more tables ofinformation, each table having a large number of entries (analogous torow of a table), each entry having multiple respective data fields(analogous to columns of the table). The function of a database query isto find all rows, for which the data in the columns of the row matchessome set of parameters defined by the query. A query may be as simple asmatching a single column field to a specified value, but is often farmore complex, involving multiple field values and logical conditions.

To support queries, a database typically includes one or more indexesfor some of the database fields. An index is a sorting of the records inone of the database tables according to the value of a correspondingfield. For example, if the database table contains records about people,one of the fields may contain a birthdate, and a corresponding indexcontains a sorting of the records by birthdate. If a query requests therecords of all persons born before a particular date, the sorted indexis used to find the responsive records, without the need to examine eachand every record to determine whether there is a match. A well-designeddatabase typically contains a respective index for each field having anordered value which is likely to be used in queries.

Execution of a query involves retrieving and examining records in thedatabase according to some search strategy. For any given logical query,not all search strategies are equal. Various factors may affect thechoice of optimum search strategy. In particular, where a logical AND ofmultiple conditions is specified, the sequential order in which theconditions are evaluated can make a significant difference in the timerequired to execute the query. The reason for this difference is thatthe first evaluated condition is evaluated with respect to all therecords in a database table, but a later evaluated condition need onlybe evaluated with respect to the subset of records for which the firstcondition was true. Similarly, for a query involving a multipleconditions conjoined by a logical OR, a later evaluated condition needonly be evaluated with respect to the subset of records for which anearlier condition was false.

To support database queries, large databases typically include a queryengine which executes the queries according to some automaticallyselected search strategy, using the known characteristics of thedatabase and other factors. Some large database applications furtherhave query optimizers which construct search strategies, and save thequery and its corresponding search strategy for reuse.

A query engine or optimizer can use the indexes and other knowncharacteristics of the database provided by the database designer.However, many large databases further support the use of user-definedfunctions in the database queries. As used herein, a user-definedfunction includes any of various functions or procedures which may beembedded in a query to provide additional capability beyond merecomparison of values from database fields. One form of user-definedfunction is computer programming code, written by the user in any ofvarious general programming languages, which returns a value in responseto one or more input parameters from a database record. Such auser-defined function may accept one or more input parameters (oftendatabase field values), which are passed with the call to the function.Although the external interface is via the passed parameter(s) and thereturned value(s), a user defined function behaves internally as anyother computer programming code. It may have arbitrary complexity,including loops, branches, calls to other functions and procedures, etc.Another form of user-defined function is a stored procedure, which is aspecial procedure built through Structured Query Language (SQL), awell-known set of database query syntax supported by many databaseapplications. A user defined function might also be a sub-query, i.e., aquery (set of query conditions) within a query, which is usually re-usedfor different queries, and provides the capability to define queries ofadded complexity. A user-defined function may have arbitrary complexity.The database designer does not know in advance what the user-definedfunctions will be, and since these are under control of the user, it isdifficult or impossible to construct indexes for user-defined functionvalues.

Because most user-defined functions involve complex evaluations whichtake longer to execute than straightforward matching of query parametersto database field values, conventional query engines and optimizerstypically evaluate user-defined functions last. I.e., any conditionsother than user-defined functions are evaluated first in order to reducethe subset of records which must be evaluated using the user-definedfunction.

If a query contains multiple user-defined functions, a conventionalengine or optimizer will typically arbitrarily choose one of theuser-defined functions to evaluate first. E.g., the engine or optimizermay evaluate the first function specified in the search string. Unlessthe user understands the workings of the database sufficiently well tocorrectly specify the search order of user-defined functions (whichrelatively few users do), such an arbitrary choice is likely to besub-optimal. If intelligent choices could be made when selecting theorder of evaluation of multiple user-defined functions, the executionperformance of such queries could be improved. A need therefore exists,not necessarily recognized, for an improved database query engine oroptimizer which can automatically make intelligent choices in orderingthe evaluation of user-defined functions.

SUMMARY OF THE INVENTION

A query engine (or optimizer) which supports database queries havinguser-defined functions maintains historical execution data with respectto each of multiple user-defined functions. The historical executiondata is dynamically updated based on query execution performance. Whenexecuting a query having one or more user-defined functions, the queryengine uses the historical execution data to predict an optimalevaluation ordering for the query conditions.

In the preferred embodiment, the historical execution data includeshistorical processor execution time of the user-defined function andhistorical yield, i.e., proportion of evaluated records which satisfiedthe query parameters for the user-defined function. These are combinedto produce a predicted execution time for different evaluationorderings, and the ordering with the lowest predicted execution time isselected as the optimal ordering. It would alternatively be possible touse other or additional data to predict an optimal ordering.

In the preferred embodiment, all query conditions not involving anyuser-defined functions are evaluated before any user-defined functions,in order to reduce the number of user-defined function evaluationsperformed. The prediction of optimal evaluation ordering is performedonly with respect to conditions which include user-defined functions,and therefore is performed only for queries having multiple user-definedfunctions. However, it would alternatively be possible to comparepredicted execution times of conditions having user-defined functionswith those not having user-defined, and in some cases to evaluate acondition having a user-defined function before a condition not having auser-defined function.

Although using historical data to predict evaluation times foruser-defined functions is an imperfect method of prediction, and willnot necessarily select the optimal evaluation ordering in everyinstance, it is an intelligent heuristic which will generally provide abetter search strategy than random guessing, and reduce the averageexecution time of database queries.

The details of the present invention, both as to its structure andoperation, can best be understood in reference to the accompanyingdrawings, in which like reference numerals refer to like parts, and inwhich:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of the major hardware components ofa computer system for optimizing the execution of database querieshaving user-defined functions, according to the preferred embodiment ofthe present invention.

FIG. 2 is a conceptual illustration of the major software components ofa computer system for optimizing the execution of database querieshaving user-defined functions, according to the preferred embodiment.

FIG. 3 is a conceptual representation of the structure of a database andassociated database indexes upon which queries are performed, accordingto the preferred embodiment.

FIG. 4 is a conceptual representation of a user-defined function historyprofile, which is used to predict an optimum query execution strategy,according to the preferred embodiment.

FIGS. 5A and 5B (herein collectively referred to as FIG. 5) are a flowdiagram illustrating at a high level the process of executing a databasequery, according to the preferred embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the Drawing, wherein like numbers denote like partsthroughout the several views, FIG. 1 is a high-level representation ofthe major hardware components of a computer system 100 for use ingenerating and executing database queries having user-defined functions,according to the preferred embodiment of the present invention. CPU 101is a general-purpose programmable processor which executes instructionsand processes data from main memory 102. Main memory 102 is preferably arandom access memory using any of various memory technologies, in whichdata is loaded from storage or otherwise for processing by CPU 101.

Memory bus 103 provides a data communication path for transferring dataamong CPU 101, main memory 102 and I/O bus interface unit 105. I/O businterface 105 is further coupled to system I/O bus 104 for transferringdata to and from various I/O units. I/O bus interface 105 communicateswith multiple I/O interface units 111-114, which may also be known asI/O processors (IOPs) or I/O adapters (IOAs), through system I/O bus104. System I/O bus may be, e.g., an industry standard PCI bus, or anyother appropriate bus technology. The I/O interface units supportcommunication with a variety of storage and I/O devices. For example,terminal interface unit 111 supports the attachment of one or more userterminals 121-124. Storage interface unit 112 supports the attachment ofone or more direct access storage devices (DASD) 125-127 (which aretypically rotating magnetic disk drive storage devices, although theycould alternatively be other devices, including arrays of disk drivesconfigured to appear as a single large storage device to a host). I/Odevice interface unit 113 supports the attachment of any of variousother types of I/O devices, such as printer 128 and fax machine 129, itbeing understood that other or additional types of I/O devices could beused. Network interface 114 supports a connection to an external network130 for communication with one or more other digital devices. Network130 may be any of various local or wide area networks known in the art.For example, network 130 may be an Ethernet local area network, or itmay be the Internet. Additionally, network interface 114 might supportconnection to multiple networks.

It should be understood that FIG. 1 is intended to depict therepresentative major components of system 100 at a high level, thatindividual components may have greater complexity than represented inFIG. 1, that components other than or in addition to those shown in FIG.1 may be present, and that the number, type and configuration of suchcomponents may vary, and that a large computer system will typicallyhave more components than represented in FIG. 1. Several particularexamples of such additional complexity or additional variations aredisclosed herein, it being understood that these are by way of exampleonly and are not necessarily the only such variations.

Although only a single CPU 101 is shown for illustrative purposes inFIG. 1, computer system 100 may contain multiple CPUs, as is known inthe art. Although main memory 102 is shown in FIG. 1 as a singlemonolithic entity, memory 102 may in fact be distributed and/orhierarchical, as is known in the art. E.g., memory may exist in multiplelevels of caches, and these caches may be further divided by function,so that one cache holds instructions while another holds non-instructiondata which is used by the processor or processors. Memory may further bedistributed and associated with different CPUs or sets of CPUs, as isknown in any of various so-called non-uniform memory access (NUMA)computer architectures. Although memory bus 103 is shown in FIG. 1 as arelatively simple, single bus structure providing a direct communicationpath among CPU 101, main memory 102 and I/O bus interface 105, in factmemory bus 103 may comprise multiple different buses or communicationpaths, which may be arranged in any of various forms, such aspoint-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, etc.Furthermore, while I/O bus interface 105 and I/O bus 104 are shown assingle respective units, system 100 may in fact contain multiple I/O businterface units 105 and/or multiple I/O buses 104. While multiple I/Ointerface units are shown which separate a system I/O bus 104 fromvarious communications paths running to the various I/O devices, itwould alternatively be possible to connect some or all of the I/Odevices directly to one or more system I/O buses.

Computer system 100 depicted in FIG. 1 has multiple attached terminals121-124, such as might be typical of a multi-user “mainframe” computersystem. Typically, in such a case the actual number of attached devicesis greater than those shown in FIG. 1, although the present invention isnot limited to systems of any particular size. User workstations orterminals which access computer system 100 might also be attached to andcommunicate with system 100 over network 130. Computer system 100 mayalternatively be a single-user system, typically containing only asingle user display and keyboard input. Furthermore, while the inventionherein is described for illustrative purposes as embodied in a singlecomputer system, the present invention could alternatively beimplemented using a distributed network of computer systems incommunication with one another, in which different functions or stepsdescribed herein are performed on different computer systems.

While various system components have been described and shown at a highlevel, it should be understood that a typical computer system containsmany other components not shown, which are not essential to anunderstanding of the present invention. In the preferred embodiment,computer system 100 is a computer system based on the IBM AS/400™ ori/Series™ architecture, it being understood that the present inventioncould be implemented on other computer systems.

FIG. 2 is a conceptual illustration of the major software components ofsystem 100 in memory 102. Operating system 201 provides variouslow-level software functions, such as device interfaces, management ofmemory pages, management and dispatching of multiple tasks, etc. as iswell-known in the art. A structured database 202 contains data which ismaintained by computer system 100 and for which the system providesaccess to one or more users, who may be directly attached to system 100or may be remote clients who access system 100 through a network using aclient/server access protocol. Database 202 contains a plurality ofrecords, each record containing at least one (and usually many) fields,as is well known in the art. Database 202 might contain almost any typeof data which is provided to users by a computer system. Associated withdatabase 202 are multiple database indexes 203-205, each indexrepresenting an ordering of records in database 202 according to somespecified criterion. Although only one database 202 and three indexes203-205 are shown in FIG. 2, the computer system may contain multipledatabases, and the number of indexes may vary (and typically is muchlarger). Alternatively, database 202 on system 100 may be logically partof a larger distributed database which is stored on multiple computersystems.

Database management system 211 provides basic functions for themanagement of database 202. Database management system 211 maytheoretically support an arbitrary number of databases, although onlyone is shown in FIG. 2. Database management system 211 preferably allowsusers to perform basic database operations, such as defining a database,altering the definition of the database, creating, editing and removingrecords in the database, viewing records in the database, definingdatabase indexes, and so forth. In particular, in accordance with thepreferred embodiment, database management system includes a query engine212 which supports the execution of queries against data in thedatabase, as more fully described herein. Database management system 211may further contain any of various more advanced database functions.Although database management system 211 is represented in FIG. 2 as anentity separate from operating system kernel 201, it will be understoodthat in some computer architectures various database managementfunctions are integrated with the operating system.

In addition to database management system 211, one or more userapplications 213, 214 executing on CPU 101 may access data in database202 to perform tasks on behalf of one or more users. Such userapplications may include, e.g., personnel records, accounting, codedevelopment and compilation, mail, calendaring, or any of thousands ofuser applications. Some of these applications may access database datain a read-only manner, while others have the ability to update data.There may be many different types of read or write database accesstasks, each accessing different data or requesting different operationson the data. For example, one task may access data from a specific,known record, and optionally update it, while another task may invoke aquery, in which all records in the database are matched to somespecified search criteria, data from the matched records being returned,and optionally updated. Furthermore, data may be read from or written todatabase 202 directly, or may require manipulation or combination withother data supplied by a user, obtained from another database, or someother source. Although two applications 213, 214 are shown forillustrative purposes in FIG. 2, the number of such applications mayvary. Applications 213, 214 typically utilize function calls to databasemanager 211 to access data in database 202, and in particular, toexecute queries to data in the database, although in some systems it maybe possible to independently access data in database 202 directly fromthe application.

System 100 further includes multiple user-defined functions (UDFs), ofwhich three 215-217 are illustrated in FIG. 2. Each user-definedfunction 215-217 is callable from the query engine and/or otherapplication program, and returns a value (or, in some cases, multiplevalues). A user-defined function may be part of a user application, ormay be a stand-alone module, as illustrated in FIG. 2. Of particularsignificance to the present invention are user-defined functions whichare used in database queries, where at least one of the passedparameters may be data (or a reference to data) in database 202. Such auser-defined function may be used almost entirely for database queries,or may be used for other purposes in addition to database queries.

In accordance with the preferred embodiment, system 100 further includesa user-defined function history profile 206. UDF profile 206 contains,for multiple user-defined functions, respective data relating toprevious execution of the function, and particularly, execution inresponse to database queries. As explained in greater detail herein, UDFprofile 206 is used by query engine 212 to predict an optimal queryexecution strategy, where a query contains user-defined functions.

Various software entities are represented in FIG. 2 as being separateentities or contained within other entities. However, it will beunderstood that this representation is for illustrative purposes only,and that particular modules or data entities could be separate entities,or part of a common module or package of modules. Furthermore, althougha certain number and type of software entities are shown in theconceptual representation of FIG. 2, it will be understood that theactual number of such entities may vary, and in particular, that in acomplex database server environment, the number and complexity of suchentities is typically much larger. Additionally, although softwarecomponents 202-206 and 211-217 are depicted in FIG. 2 on a singlecomputer system 100 for completeness of the representation, it is notnecessarily true that all programs, functions and data will be presenton a single computer system or will be performed on a single computersystem. For example, user applications may be on a separate system fromthe database; a database may be distributed among multiple computersystems, so that queries against the database are transmitted to remotesystems for resolution, and so forth.

While the software components of FIG. 2 are shown conceptually asresiding in memory 102, it will be understood that in general the memoryof a computer system will be too small to hold all programs and datasimultaneously, and that information is typically stored in data storagedevices 125-127, comprising one or more mass storage devices such asrotating magnetic disk drives, and that the information is paged intomemory by the operating system as required. In particular, database 202is typically much too large to be loaded into memory, and typically onlya small portion of the total number of database records is loaded intomemory at any one time. The full database 202 is typically recorded indisk storage 125-127. Furthermore, it will be understood that theconceptual representation of FIG. 2 is not meant to imply any particularmemory organizational model, and that system 100 might employ a singleaddress space virtual memory, or might employ multiple virtual addressspaces which overlap.

FIG. 3 is a conceptual representation of the structure of database 202and associated database indexes 203, 204, containing data which can beanalyzed by executing a logical query, according to the preferredembodiment. Database 202 comprises one or more database tables 301 (ofwhich only one is shown in FIG. 3). Each table contains multipledatabase records 302, each record containing multiple data valueslogically organized as multiple data fields 303-306. Database 202 isconceptually represented in FIG. 3 as a table or array, in which therows represent database records, and the columns represent data fields.However, as is well known in the art, the actual structure of thedatabase in memory typically varies due to the needs of memoryorganization, accommodating database updates, and so forth. A databasewill often occupy non-contiguous blocks of memory; database records mayvary in length; some fields might be present in only a subset of thedatabase records; and individual records may be non-contiguous. Portionsof the data may even be present on other computer systems. Variouspointers, arrays, and other structures (not shown) may be required toidentify the locations of different data contained in the database.

Because database 202 may contain a very large number of records, and itis frequently necessary to access these records in some logical (sorted)order, database indexes 203-205 provide a pre-sorted ordering of thedatabase records according to some logical criterion. Typically, anindex sorts the database according to the value of a specific field, thefield being used to sort the database varying with the index. FIG. 3represents two indexes 203, 204, where index 203 sorts database recordsaccording to the value of field A 303, and index 204 sorts databaserecords according to the value of field B 304.

Conceptually, each index contains a plurality of entries 311A, 311B(herein generically referred to as feature 311), each entry 311corresponding to a respective entry 302 in a database table 301, andcontaining a reference 312A, 312B (herein generically referred to asfeature 312) and a value 313A,313B (herein generically referred to asfeature 313). The reference 312 is a reference to the correspondingentry 302 in database table 301. A reference could be a pointer, arrayindex, record number, etc., which enables one to identify and access thecorresponding database entry. The value 313 is the value from the fieldsorted by the index for the corresponding database entry. E.g., forindex 203, which sorts database records according to the value of fieldA 303, the value 313A is the value of field A 303 for each correspondingdatabase record. For an index, the entries are sorted so that values 313are in a sorted order. Although indexes 203, 204 are representedconceptually in FIG. 3 as tables or arrays, a different structure, suchas a binary tree, is typically used due to the need to update theindexes responsive to database updates, and to quickly identify thelocation of a desired value in the sorted order.

In accordance with the preferred embodiment, query engine 212 executeslogical queries of database 202 using indexes 203-205. At least some ofthese queries include one or more user defined functions 215-217. I.e.the query requires evaluation of a logical statement which is performedby calling and executing one or more user-defined functions with respectto each of a plurality of database records. Where multiple user-definedfunctions are contained in a query, the resources (e.g. processor time)required to execute the query can depend on the order in whichuser-defined functions are evaluated. In order to select an optimumorder of evaluation, the query engine looks at historical profile dataregarding previous invocations of the user-defined functions, and usesthis data to predict a optimum order of evaluation.

FIG. 4 is a conceptual representation of the user-defined functionhistory profile 206 which is used by the query engine to predict anoptimum query execution strategy, according to the preferred embodiment.Referring to FIG. 4, the UDF profile 206 comprises a plurality ofentries 401, each entry corresponding to a respective user-definedfunction 215-217. Each entry 401 contains a function reference field402, a cumulative cost field 403, an evaluated field 404, and a selectedfield 405. Function reference field 402 uniquely identifies theuser-defined function to which the profile entry 401 corresponds; thisfield could be a file name, a pointer, or any other data which can beused in the applicable computer system's architecture to uniquelyidentify the user-defined function.

Cumulative cost field 403 is a measure of the cumulative historical“cost” of executing the user-defined function. Cost is a generalmeasurement reflecting the quantity of some resource or resources used.Cost can be metered in any of a variety of ways. In the preferredembodiment, cost is a measure of CPU time or CPU cycles required toexecute the user-defined function. However, cost could alternativelytake into account other or additional measures of consumed resources,such as response time, number of storage accesses, network bandwidthconsumed, and so forth. Where resources on different computer systems ina distributed network are consumed, cost could be weighted for eachsystem according to some suitable weighting criteria. Cost field 403 isan accumulation counter, which is incremented each time the user-definedfunction is executed.

Evaluated field 404 is a cumulative count of the number of times theuser-defined function is invoked to evaluate some set of inputparameters from database 202. Like cost field 403, evaluated field isincremented each time the user-defined function is executed, andtherefore the quotient of cost field 403 divided by evaluated field 404yields the average historical cost per invocation of the user-definedfunction.

Selected field 405 is a cumulative count of the number of times a parsedcondition term of a logical expression containing a user-definedfunction within a query evaluates to “true”. Like the cost field 403 andevaluated field 404, the selected field 405 is incremented with eachquery. The ratio of the selected field value to the evaluated fieldvalue is the historical average percentage of times that such a parsedterm of a logical expression evaluated to “true”. For predictivepurposes before the query is executed, this historical average isassumed to be the probability that such a parsed term within a querywill evaluate to “true”.

For example, if Query Q is parsed into multiple terms conjoined by alogical AND operator, and contains a term of the form UDFx(F₁,F₂)>K,where UDFx is a user-defined function, F1 and F2 are fields within arecord in database 202, and K is a constant, then Query Q is executed byevaluating UDFx(F₁,F₂) with respect to multiple records in database 202.If UDFx(F₁,F₂) is evaluated with respect to N records, and UDFx(F₁,F₂)>Kfor M of those N records, then evaluated field 404 is incremented by Nand selected field is incremented by M. Cost field 403 is incremented bythe total CPU time required to execute the N function calls to UDFx.

In the preferred embodiment, cost field 403, evaluated field 404, andselected field 405 are incremented only when the user-defined functionis invoked by the query engine 212 to satisfy a database query. It willbe recognized that a user-defined function might be invoked in othercircumstances, e.g., by an application program, to display or updatedata in database 202. However, the purpose of UDF profile 206 is toprovide data for predicting the performance of queries, and for thisreason historical query data is considered most representative.Additionally, in the absence of a query, selected field 405 has littlemeaning. It would alternatively be possible to take into accounthistorical performance of a user-defined function when invoked for apurpose other than a query.

In the preferred embodiment, in order to reflect recent historicaltrends in user-defined function performance, and to avoid overruns inthe cumulative counters, data in fields 403-405 is periodically aged.Preferably, aging involves multiplying each field 403-405 in a profileentry 401 by a constant between 0 and 1. Aging could be performed inresponse to any of various events, alone or in combination, such as oneof the cumulative counters reaching a pre-defined threshold value, theexpiration of a timer, or some pre-determined number of queries havingbeen performed. Aging might alternatively be performed each time a queryis run which references the applicable user-defined function.

Among the functions supported by database management system 211 is themaking of queries against data in database 202, which are executed byquery engine 212. As is known, queries typically take the form ofstatements having a defined format, which test records in the databaseto find matches to some set of logical conditions. Typically, multipleterms, each expressing a logical condition, are connected by logicalconjunctives such as “AND” and “OR”. Because database 202 may be verylarge, having a very large number of records, and a query may be quitecomplex, involving multiple logical conditions, it can take some timefor a query to be executed against the database, i.e., for all thenecessary records to be reviewed and to determine which records, if any,match the conditions of the query.

The amount of time required to perform a complex query on a largedatabase can vary greatly, depending on many factors. Depending on howthe data is organized and indexed, and the conditions of the query, itmay be desirable to evaluate records in a particular order, specificallyto evaluate certain logical conditions before evaluating other logicalconditions. For example, if a query contains a set of terms connected bylogical AND conjunctives and one assumes sequential evaluation of terms,then a first term to be evaluated must be evaluated against all recordsin a database table, but a second term to be evaluated must only beevaluated against those records for which the first term evaluated to“true”, and subsequent terms need only be evaluated against thoserecords for which all prior terms evaluated to “true”. In general, thenumber of records evaluated for a particular term decreases as the termcomes later in the order of evaluation, and therefore, as a generalrule, it is desirable to evaluate terms which can be evaluated quicklyfirst, and to evaluate terms which require a greater amount of systemresource later. However, another consideration is the proportion ofrecords which do not satisfy the logical condition, and therefore do notneed to be evaluated for subsequent terms. E.g., if 50% of the recordsfail to satisfy a first condition term, and 99% of the records fail tosatisfy a second condition term, then it is generally desirable toevaluate the second condition first, since it means that the firstcondition need only be evaluated with respect to 1% of the records. Boththese considerations, and possibly others, should be taken into accountin selecting an optimum query execution strategy.

It will be noted that logical condition terms joined by the conjunctive“OR” can be converted to conditions joined by “AND” by negating theconditions, and again negating the logical conjunction of theconditions, and therefore similar principles apply. Furthermore,multiple conditions could be combined by a conjunctive to form a singlecondition which is part of a larger set of conditions at a differentlevel of nesting. Well known techniques exist for dissecting queriesinvolving combinations of different conjunctions and different nestinglevels.

Query engine 212 automatically generates an execution strategy forperforming a database query. FIGS. 5A and 5B (herein collectivelyreferred to as FIG. 5) are a flow diagram illustrating at a high levelthe process of executing a database query, according to the preferredembodiment.

Referring to FIG. 5, a requesting user formulates and submits a databasequery using any of various techniques now known or hereafter developed(step 501). E.g., the database query might be constructed and submittedinteractively using a query interface in database management system 211,might be submitted from a separate interactive query applicationprogram, or might be embedded in a user application and submitted by acall to the query engine 212 when the user application is executed. Aquery might be submitted from an application executing on system 100, ormight be submitted from a remote application executing on a differentcomputer system.

In response to receiving the query, query engine 212 parses the queryinto logical conditions (step 502), there being multiple conditions forall but the simplest of queries. Parsing step 502 produces a list ofconditions to be evaluated in a logical query representation, which mayinclude nested sub-lists of conditions within the list of conditions.The query engine analyzes the list of conditions and partially sorts thelist in an evaluation order (step 503), i.e., in the order in which theconditions are to be evaluated. The list is partially sorted by (a)moving all conditions involving at least one user-defined function tothe end of the sorting order, and (b) sorting all remaining conditionsaccording to any algorithm, now known or hereafter developed, foroptimizing the order of evaluation of conditions in a database query.Where nested sub-lists of conditions are involved, if at least oneuser-defined function appears in the sub-list, the entire sub-list ismoved to the end of the sorting order. However, within any suchsub-list, any conditions not involving a user-defined function areplaced in sorted order ahead of conditions involving a user-definedfunction.

The query engine initializes an evaluation loop counter (EC) to a valuein excess of a loop threshold, so that a procedure for determining theorder of evaluation of user-defined functions as described herein istriggered initially, and further initializes arrays of CostX, EvalX andSelX values to zero (step 504). Each array CostX, EvalX, and SelXcontains a value corresponding to each respective user-defined functioncontained in the query. The arrays are used to dynamically adjust theorder of evaluation during query execution, as explained more fullyherein.

In some cases, the query engine evaluates one or more logicalconditions, not involving user-defined functions, with respect to allthe records (represented as optional step 505). Specifically, where acondition involves an indexed value which can be rapidly evaluated byreference to the index, without examining all the records, it may bepreferable to “evaluate” this condition first by generating a list ofrecords satisfying the condition from the sorted index. Whether it ispreferable to do so will depend on the number of records selected andother factors. Conventional algorithms exist for making such adetermination. There could be multiple such indexed conditions, in whichcase the lists of records from multiple indexes would be logically ANDedbefore proceeding.

The query engine then decides whether it is necessary to determine orre-determine the order of evaluation of user-defined function conditions(step 506). If there are at least two user-defined function conditions,and the evaluation loop counter (EC) meets or exceeds some pre-definedthreshold, then the ‘Y’ branch from step 506 is taken to determine theorder of evaluation, as shown in steps 521-529; otherwise these stepsare skipped. The threshold is simply an arbitrary number which causesthe order of evaluation to be re-determined some periodic number of loopcounts. The threshold could be as low as 1, forcing a re-determinationwith every iteration of the loop, but since some overhead is associatedwith re-determining the order of evaluation, the threshold is preferablysome number substantially greater than 1, e.g., 1000. Alternatively, itwould be possible to determine the order of evaluation of user-definedfunctions once and only once at the beginning of a query, and not tore-determine the order.

To sort the conditions involving user-defined functions, theuser-defined function conditions are initially ordered in search-stingorder (step 521), assuming that this was not already performed as partof step 503. I.e., the user-defined function conditions are placed inthe order in which they are expressed by the requesting user in thequery, but at the end of the list after conditions not involvinguser-defined functions.

The query engine analyzes the list of conditions to find the deepestnesting level having a sub-list with multiple user-defined functions(step 522). The query engine then selects a list or sub-list at thedeepest nesting level (step 523). Where the deepest nesting level is thetop level (which is often the case), the entire top level list isselected. If the selected list or sub-list does not contain more thanone UDF condition, sorting is unnecessary, and the query engine proceedsto step 526.

If the selected list or sub-list contains multiple UDF conditions (the‘Y’ branch from step 524), the query engine then sorts the UDFconditions in the selected list or sub-list using a bubble sortalgorithm (step 525). To use a bubble sort, the query engine comparespairs of UDF conditions to determine which condition of a pair should beevaluated first, the condition to be first evaluated being placedearlier in the sorting order. For conditions involving only a single UDF(e.g., in which the value of a UDF is compared to a constant, a value ofa database field, a system variable, etc.), this determination comparesthe values:

$\begin{matrix}{{\frac{{Cost}\left( {UDF}_{A} \right)}{{Eval}\left( {UDF}_{A} \right)} + \left\lbrack {\frac{{Sel}\left( \left( {UDF}_{A} \right) \right.}{{Eval}\left( {UDF}_{A} \right)}*\frac{{Cost}\left( {UDF}_{B} \right)}{{Eval}\left( {UDF}_{B} \right)}} \right\rbrack};{and}} & (1) \\{{\frac{{Cost}\left( {UDF}_{B} \right)}{{Eval}\left( {UDF}_{B} \right)} + \left\lbrack {\frac{{Sel}\left( \left( {UDF}_{B} \right) \right.}{{Eval}\left( {UDF}_{B} \right)}*\frac{{Cost}\left( {UDF}_{A} \right)}{{Eval}\left( {UDF}_{A} \right)}} \right\rbrack};} & (2)\end{matrix}$where Cost(UDF_(A)) is the cumulative cost associated with User DefinedFunction A (UDF_(A)), Sel(UDF_(A)) is the cumulative number selectedassociated with UDF_(A), and Eval(UDF_(A)) is the cumulative numberevaluated associated with UDF_(A), and similarly for User DefinedFunction B.

For the initial determination, the cumulative cost associated withUDF_(A) is the value from field 403 of the entry in UDF profile 206corresponding to UDF_(A), the cumulative number selected is the valuefrom field 405 of the entry in UDF profile 206 corresponding to UDF_(A),and the cumulative number evaluated is the value from field 404 of theentry in UDF profile 206 corresponding to UDF_(A), and similarly forUser Defined Function B. However, for subsequent re-determinationsduring query execution triggered by a pre-determined number of loopiterations having been reached, these numbers are weighted averages ofthe initial values from UDF profile 206 and dynamically updated valuesfrom the arrays of CostX, EvalX, and SelX values corresponding to eachrespective user-defined function. Any suitable weighting formula can beused, and preferably the relative weight of the values from the CostX,EvalX and SelX arrays increases with increasing numbers of evaluationsperformed in the current query. For sufficiently large numbers ofiterations or evaluations, the weighting formula might assign 100%weight to the values from the CostX, EvalX and SelX arrays. By thususing data from the current query, the most reliable, up-to-datehistorical performance data is used. This is particularly significant inthe case of the number selected value, since such a value is veryquery-dependent. Notwithstanding the fact that queries run in the pasthave yielded a certain average value of number selected, the currentquery might be substantially different, and dynamic updating of thehistorical values permits greater accuracy in determining an optimumorder of evaluation. As used herein, “historical” cost, evaluation,selected, and other function performance data includes either or bothrecent historical performance data (e.g., from the CostX, EvalX and SelXarray) as well as more remote historical performance data (from the UDFprofile 206).

The term Cost(UDF_(A))/Eval(UDF_(A)) reflects the average historical“cost” for each invocation of UDF_(A). If UDF_(A) is evaluated beforeUDF_(B), then UDF_(B) need only be evaluated with respect to the shareof database entries for which the condition involving UDF_(A) is true.Based on historical data, this share is estimated to beSel(UDF_(A))/Eval(UDF_(A)). Therefore, if term (1) above is less thanterm (2), UDF_(A) should be evaluated first and is placed before UDF_(B)in the evaluation order (and vice versa).

It will be recognized that expressions (1) and (2) above can besimplified for computational purposes by algebraic manipulation tocompare the quantities:Cost(UDF_(A))*Eval(UDF_(B))+Sel(UDF_(A))*Cost(UDF_(B)); andCost(UDF_(B))*Eval(UDF_(A))+Sel(UDF_(B))*Cost(UDF_(A)).Alternatively, data may be stored in UDF profile 206 in a pre-computedform (e.g., as a pre-computed ratio). Other algebraic manipulations maybe possible to simplify the required computations.

The basic principles above can be extended to those cases where a singlelogical condition involves multiple user-defined functions. In suchcases, the cost of evaluating the condition is the sum of the averagecost for each of the user-defined functions. E.g., if a logicalcondition is of the form UDF_(X)>UDF_(Y), then the cost of evaluatingthis condition is the sum of the costs of evaluating UDF_(X) andUDF_(Y). In this case the proportion selected is much more difficult topredict. Preferably, the lower of the two UDF selected proportions isused as an approximation.

Although a specific formula is described herein as a preferredembodiment, it will be recognized that numerous variations are possiblein the type of data maintained in UDF profile 206 or the dynamic arraysCostX, EvalX and SelX, and the predictive formula used to determine theordering of user-defined function evaluations. For example, UDF profilecould additionally maintain data relating to the variance or standarddeviation of cost and proportion selected. Additional parameters mightbe of further predictive value.

When the list or sub-list is completely sorted, the query engineproceeds to step 526. If the just sorted list was the top level list(the ‘Y’ branch from step 526), sorting is finished and the query engineresets the evaluation loop counter (EC) (step 529), and proceeds to step507 to select the next record to evaluate. If not the query enginedetermines whether any more sub-lists exist at the same nesting level(step 527). If another sub-list exists at this level (the ‘Y’ branchfrom step 527), the query engine selects the next sub-list (step 523),and sorts the selected sub-list using the procedure described above withrespect to step 524-525. Otherwise, the query engine moves up one levelof nesting (step 528), and selects a list or sub-list from the new levelfor sorting (step 523).

When sorting of the UDF conditions is completed as described above, thequery engine selects a next database record for evaluation (step 507),and evaluates the logical conditions with respect to the selected recordin the pre-determined order (step 508), using any known technique ortechnique hereafter developed. In general, for user-defined functions,evaluation means that the user-defined function will be separatelycalled in the pre-determined order, until a logical result is determinedor all conditions evaluated. E.g., for a conjunction of logical ANDs,each successive condition is evaluated until a condition returns “false”(which obviates the need to evaluate any further conditions) or untilall conditions are evaluated.

The query engine updates the CostX, EvalX and SelX arrays byincrementing the corresponding CostX and EvalX array values for anyuser-defined functions which were actually evaluated, and thecorresponding SelX values for any user-defined functions for which thecondition evaluated to “true” (step 509).

If any further records remain to be evaluated, the ‘Y’ branch is takenfrom step 510, the evaluation loop counter is incremented (step 511),and the query engine returns to the head of the evaluation loop at step506. When all records have been evaluated, the query engine proceeds tostep 512.

With the evaluations complete, the query engine updates UDF profile 206by incrementing the various accumulation counters with the cost,selected and evaluated counts of each user-defined function called inthe query (step 512). Preferably, these counts are maintained as programvariables CostX, EvalX and SelX in memory during the execution.

The query engine then generates and returns results in an appropriateform (step 513). E.g., where a user issues an interactive query, thistypically mean returning a list of matching database entries for displayto the user. A query from an application program may perform some otherfunction with respect to database entries matching a query. Althoughstep 513 is represented in FIG. 5 as occurring after all evaluation hasbeen performed, in many applications partial query results will bereturned to the user as they are generated (e.g., just before step 510).

The process of executing a query is shown and described herein as asingle-threaded process for clarity of illustration and understanding.Although such a process could indeed be executed as a single thread, itwill be recognized by those skilled in the art that queries may also beexecuted as multi-threaded processes, either on a single processor ormultiple processors, and multiple user-defined functions may be runningin parallel during the query execution. Notwithstanding that somethreads execute in parallel, it is still advantageous for variousreasons to order the UDFs in the most efficient logical order asdescribed herein. It is unlikely that all conditions are evaluatedsimultaneously and independently by separately running threads, and tothe extent the evaluation of any condition is dependent on or seriallyfollows another, it makes sense to order the evaluation of conditions inan optimal logical order. The present invention is not limited tosingle-threaded environments, or to any particular number of threads orprocesses.

In the preferred embodiment described above, the generation andexecution of the query is described as a series of steps in a particularorder. However, it will be recognized by those skilled in the art thatthe order of performing certain steps may vary, and that variations inaddition to those specifically mentioned above exist in the wayparticular steps might be performed. In particular, in some environmentsdatabase queries are analyzed and compiled into an intermediaterepresentation or set of callable or executable instructions, and aresaved for use multiple times. In these cases, the steps of formulatingthe query and parsing it into a logical representation might beperformed long before the query is executed. In such an environment, theorder of execution might be determined once at compile time for multipleexecutions, or it might be deferred until execution time (and thusdetermined separately for each execution) to obtain the full benefit ofalgorithms which take into account changes to the database.

In the preferred embodiment, historical data from previous queries ismaintained in a profile, and is averaged on a weighted basis withhistorical data from the current query during execution to dynamicallyadjust the evaluation order. However, it would alternatively be possibleto use only historical data from previous queries in determining theorder of execution, thus determining the order only once at thebeginning of a query. This alternative may be desirable where, as insome database architectures, evaluation is performed by sequentiallyevaluating a first user-defined function with respect to all recordsbefore evaluating a second user-defined function. As a furtheralternative, it would be possible to use only historical data from thecurrent query, without maintaining a profile of performance data fromprevious queries.

In general, the routines executed to implement the illustratedembodiments of the invention, whether implemented as part of anoperating system or a specific application, program, object, module orsequence of instructions, are referred to herein as “programs” or“computer programs”. The programs typically comprise instructions which,when read and executed by one or more processors in the devices orsystems in a computer system consistent with the invention, cause thosedevices or systems to perform the steps necessary to execute steps orgenerate elements embodying the various aspects of the presentinvention. Moreover, while the invention has and hereinafter will bedescribed in the context of fully functioning computer systems, thevarious embodiments of the invention are capable of being distributed asa program product in a variety of forms, and the invention appliesequally regardless of the particular type of signal-bearing media usedto actually carry out the distribution. Examples of signal-bearing mediainclude, but are not limited to, recordable type media such as volatileand non-volatile memory devices, floppy disks, hard-disk drives,CD-ROM's, DVD's, magnetic tape, and transmission-type media such asdigital and analog communications links, including wirelesscommunications links. An example of signal-bearing media is illustratedin FIG. 1 as system memory 102, and as data storage devices 125-127.

Although a specific embodiment of the invention has been disclosed alongwith certain alternatives, it will be recognized by those skilled in theart that additional variations in form and detail may be made within thescope of the following claims:

1. A method for database query optimization in a computer system,comprising the steps of: maintaining historical execution data withrespect to each of a plurality of user-defined functions, saidhistorical execution data comprising respective at least one measure ofaverage cost of evaluation of each of said plurality of user-definedfunctions; receiving a database query having a plurality of logicalconditions, said database query including at least one of saiduser-defined functions in at least one of said logical conditions; usingthe respective at least one measure of average cost of evaluation fromsaid historical execution data of each user-defined function containedin said query to predict an optimal ordering of evaluation of saidplurality of logical conditions; and evaluating said database query inaccordance with said predicted optimal ordering of evaluation of saidplurality of logical conditions.
 2. The method for database queryoptimization of claim 1, wherein said database query includes aplurality of said user-defined functions, and wherein said step of usingsaid historical execution data to predict an optimal ordering ofevaluation comprises using said historical execution data to predict anoptimal ordering of evaluation of said plurality of user-definedfunctions included in said database query.
 3. The method for databasequery optimization of claim 1, wherein said historical execution datafurther comprises average proportion of database query logicalconditions evaluating to “true” for each of said user-defined functions,and wherein the method further uses the respective average proportion ofdatabase query logical conditions evaluating to “true” from saidhistorical execution data of each user-defined function contained insaid query to predict said optimal ordering of evaluation of saidplurality of logical conditions.
 4. The method for database queryoptimization of claim 1, wherein said historical execution datacomprises data from evaluation of said user-defined functions insatisfying said database query.
 5. The method for database queryoptimization of claim 4, wherein said step of using historical executiondata to predict an optimal ordering of evaluation comprises:periodically re-determining an optimal ordering of evaluation aplurality of times while evaluating said database query, wherein eachsuccessive periodic re-determination of an optimal ordering uses saiddata from evaluation of said user-defined functions in satisfying saiddatabase query, said data from evaluation of said user-defined functionsin satisfying said database query being updated from each previousperiodic re-determination of an optimal ordering.
 6. The method fordatabase query optimization of claim 4, wherein said step of using therespective at least one measure of average cost of evaluation from saidhistorical execution data of each user-defined function contained insaid query to predict an optimal ordering of evaluation of saidplurality of logical conditions comprises using a weighted average ofsaid data from evaluation of said user-defined functions in satisfyingsaid database query and data from execution of said user-definedfunctions prior to evaluating said database query.
 7. Acomputer-readable storage medium encoded with a computer program fordatabase query optimization comprising: a plurality of processorexecutable instructions recorded on tangible computer-readable media,wherein said instructions, when executed by at least one processor of atleast one computer system, cause the at least one computer system toperform the steps of: maintaining historical execution data with respectto each of a plurality of user-defined functions; receiving a databasequery having a plurality of logical conditions, said database queryincluding at least one of said user-defined functions in at least one ofsaid logical conditions; using said historical execution data to predictan optimal ordering of evaluation of said plurality of logicalconditions; and evaluating said database query in accordance with saidpredicted optimal ordering of evaluation of said plurality of logicalconditions.
 8. The computer program product of claim 7, wherein saiddatabase query includes a plurality of said user-defined functions, andwherein said step of using said historical execution data to predict anoptimal ordering of evaluation comprises using said historical executiondata to predict an optimal ordering of evaluation of said plurality ofuser-defined functions included in said database query.
 9. The computerprogram product of claim 7, wherein said historical execution datacomprises average cost of evaluation of each of said user-definedfunctions.
 10. The computer program product of claim 7, wherein saidhistorical execution data comprises average proportion of database querylogical conditions evaluating to “true” for each of said user-definedfunctions.
 11. The computer program product of claim 7, wherein saidhistorical execution data comprises data from evaluation of saiduser-defined functions in satisfying said database query.
 12. Thecomputer program product of claim 11, wherein said step of usinghistorical execution data to predict an optimal ordering of evaluationcomprises using a weighted average of said data from evaluation of saiduser-defined functions in satisfying said database query and data fromexecution of said user-defined functions prior to evaluating saiddatabase query.
 13. The computer program product of claim 7, whereinsaid historical execution data comprises data from execution of saiduser-defined functions prior to evaluating said database query.
 14. Thecomputer program product of claim 7, wherein said step of using saidhistorical execution data to predict an optimum order of evaluationcomprises predicting that logical conditions not containing user-definedfunctions be evaluated first, and, among multiple logical conditionscontaining respective user-defined functions, predicting an optimalorder of evaluation using said historical execution data.
 15. A computersystem, comprising: at least one processor; a data storage for storing adatabase; a database management facility embodied as a plurality ofinstructions executable on said at least one processor, said databasemanagement facility including a query engine which executes logicalqueries against said database, each logical query containing arespective set of logical conditions, at least some of said respectivesets of logical conditions containing one or more logical conditionsincluding respective one or more user-defined functions; wherein saiddatabase management facility automatically maintains historicalexecution data for each of a plurality of said user-defined functions,said historical execution data including a respective relative averageevaluation cost for each of said plurality of said user-definedfunctions; wherein said database management facility automaticallypredicts a respective optimal ordering of evaluation of each of aplurality of said respective sets containing one or more logicalconditions including respective one or more user-defined functions usingrespective historical execution data for each said user-definedfunction, and automatically executes the query containing the respectiveset according to the predicted optimal ordering of evaluation.
 16. Thecomputer system of claim 15, wherein said historical execution datafurther comprises a respective average proportion of logical conditionsevaluating to “true” for each of said user-defined functions.
 17. Thecomputer system of claim 15, wherein said historical execution data usedfor predicting an optimal ordering of evaluation of each respectivequery comprises data from evaluation of said user-defined functions insatisfying the respective query.
 18. The computer system of claim 15,wherein said historical execution data comprises data from execution ofsaid user-defined functions prior to evaluating said database query. 19.The computer system of claim 15, wherein said step of using saidhistorical execution data to predict an optimum order of evaluationcomprises predicting that logical conditions not containing user-definedfunctions be evaluated first, and, among multiple logical conditionscontaining respective user-defined functions, predicting an optimalorder of evaluation using said historical execution data.